Merge pull request #488 from Unity-Technologies/docs/random-fixes

Fixed code formating and links.

Author: Marwan Mattar

docs/Background-Unity.md

@@ -10,9 +10,10 @@ with ML-Agents:
 * [Editor](https://docs.unity3d.com/Manual/UsingTheEditor.html)
 * [Interface](https://docs.unity3d.com/Manual/LearningtheInterface.html)
 * [Scene](https://docs.unity3d.com/Manual/CreatingScenes.html)
-* [GameObjects](https://docs.unity3d.com/Manual/GameObjects.html)
+* [GameObject](https://docs.unity3d.com/Manual/GameObjects.html)
 * [Rigidbody](https://docs.unity3d.com/ScriptReference/Rigidbody.html)
 * [Camera](https://docs.unity3d.com/Manual/Cameras.html)
 * [Scripting](https://docs.unity3d.com/Manual/ScriptingSection.html)
+* [Physics](https://docs.unity3d.com/Manual/PhysicsSection.html)
 * [Ordering of event functions](https://docs.unity3d.com/Manual/ExecutionOrder.html)
 (e.g. FixedUpdate, Update)

docs/Feature-Memory.md

@@ -1,4 +1,4 @@
-# Using Recurrent Neural Networks in ML-Agents
+# Memory-enhanced Agents using Recurrent Neural Networks
 
 ## What are memories for?
 Have you ever entered a room to get something and immediately forgot
@@ -19,10 +19,10 @@ track of what is important to remember with [LSTM](https://en.wikipedia.org/wiki
 When configuring the trainer parameters in the `trainer_config.yaml` 
 file, add the following parameters to the Brain you want to use.
 
-```
-	use_recurrent: true
-	sequence_length: 64
-	memory_size: 256
+```json
+use_recurrent: true
+sequence_length: 64
+memory_size: 256
 ```
 
 * `use_recurrent` is a flag that notifies the  trainer that you want 
@@ -43,5 +43,3 @@ and Unity, using too large `memory_size` will slow down training.
 * Adding a recurrent layer increases the complexity of the neural 
 network, it is recommended to decrease `num_layers` when using recurrent.
 * It is required that `memory_size` be divisible by 4.
-
-

docs/Feature-Monitor.md

@@ -6,7 +6,7 @@ The monitor allows visualizing information related to the agents or training pro
 
 You can track many different things both related and unrelated to the agents themselves. To use the Monitor, call the Log function anywhere in your code :
 ```csharp
-   Monitor.Log(key, value, displayType , target)
+Monitor.Log(key, value, displayType , target)
 ```
  * *`key`* is the name of the information you want to display.
  * *`value`* is the information you want to display.

docs/Getting-Started-with-Balance-Ball.md

@@ -269,10 +269,8 @@ on the same graph.
 
 To summarize, go to your command line, enter the `ml-agents` directory and type: 
 
-```
-
+```python
 python3 python/learn.py <env_file_path> --run-id=<run-identifier> --train 
-
 ```
 
 The `--train` flag tells ML-Agents to run in training mode. `env_file_path` should be the path to the Unity executable that was just created. 
@@ -282,7 +280,7 @@ The `--train` flag tells ML-Agents to run in training mode. `env_file_path` shou
 
 Once you start training using `learn.py` in the way described in the previous section, the `ml-agents` folder will 
 contain a `summaries` directory. In order to observe the training process 
-in more detail, you can use TensorBoard. From the command line run :
+in more detail, you can use TensorBoard. From the command line run:
 
 `tensorboard --logdir=summaries`
 

docs/Installation-Windows.md

@@ -174,9 +174,7 @@ pip install tensorflow-gpu
 
 Lastly, you should test to see if everything installed properly and that TensorFlow can identify your GPU. In the same Anaconda Prompt, type in the following command:
 
-```
-python
-
+```python
 import tensorflow as tf
 
 sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))